72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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from numpy.random import choice, seed
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from sklearn.decomposition import PCA
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from sklearn.preprocessing import Normalizer
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seed(42)
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def initialize_medoids(num_medoids, data):
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return [data.iloc[idx] for idx in choice(len(data), size=num_medoids, replace=False)]
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def assign_points_to_medoids(data, medoids):
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return [np.argmin([distance_vec2vec(point[1], medoid) for medoid in medoids]) for point in data.iterrows()]
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def distance_vec2vec(a, b) -> np.float64:
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return sum([(abs(a[i] - b[i]) ** 2) for i in range(len(a))])
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def reassign_medoids(data, assignments, initial_medoids):
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new_medoids = []
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for idm, medoid in enumerate(initial_medoids):
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new_medoid = medoid
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medoid_score = sum([distance_vec2vec(medoid, x[1]) if assignments[idx] == idm else 0
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for idx, x in enumerate(data.iterrows())])
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for point in data.iterrows():
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point_score = sum(sum([distance_vec2vec(point, x[1]) if assignments[idx] == idm else 0
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for idx, x in enumerate(data.iterrows())]))
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if medoid_score > point_score:
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new_medoid = point
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new_medoids.append(new_medoid)
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return new_medoids
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def is_finished(old_medoids, new_medoids):
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return set([tuple(om) for om in old_medoids]) == set([tuple(nm) for nm in new_medoids])
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def kmedoids(num_samples, num_clusters):
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df = pd.read_csv('CC GENERAL.csv', index_col='CUST_ID')
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df = df[:num_samples].fillna(0)
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df_scaled = pd.DataFrame(Normalizer().fit_transform(df))
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# initialize medoids (at random)
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medoids = initialize_medoids(num_medoids=num_clusters, data=df_scaled)
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# assign data points to the medoids
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assignments = assign_points_to_medoids(data=df_scaled, medoids=medoids)
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# fit
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new_medoids = reassign_medoids(data=df_scaled, assignments=assignments, initial_medoids=medoids)
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while not is_finished(old_medoids=medoids, new_medoids=new_medoids):
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medoids = new_medoids
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new_medoids = reassign_medoids(data=df_scaled, assignments=assignments, initial_medoids=medoids)
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new_assignments = assign_points_to_medoids(data=df_scaled, medoids=new_medoids)
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data = pd.DataFrame(PCA(n_components=2).fit_transform(df_scaled), columns=['0', '1'])
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data['cluster'] = new_assignments
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sns.relplot(x='0', y='1', hue='cluster', data=data, palette=sns.color_palette("husl", num_clusters))
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plt.show()
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for i in range(2, 8):
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kmedoids(num_samples=500, num_clusters=i)
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print(i)
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